Managers of commercial establishments, such as retail stores, shopping malls, transportation centers and the like, responsible for maximizing sales of products and services, are well aware that the layout of their facilities has a substantial impact on sales volume. To evaluate this impact, it is necessary to gather data characterizing the flow of customer traffic into and within the facility. This data will reveal the locations where customers are present more frequently (“hot spots”) and those where customer traffic is lighter (“cold spots”).
With this information, it is possible for the manager to make changes in features that affect accessibility, lighting, fixture space, product placement, and the like that will improve product exposure and reduce the number and/or size of cold spots. After such changes have been made, the manager will often wish to conduct a further traffic flow study to assess the effectiveness of these changes.
The tracking data, along with product placement data are also important to distributors of products sold in commercial establishments. This information enables them to evaluate whether their products are receiving sufficient attention in a commercial establishment, so that the cost of shelf space, among other things, is justified. It also enables them to assess whether they should request shelf space for their products in a different location in the commercial establishment. Tracking data also enables distributors and other entities to better understand particularly how customers shop within commercial establishments, and also may be used to better understand operations and activities of competitive businesses.
Traditionally such traffic flow studies have been conducted manually. One or more of the manager's employees would record the movements of customers within the facility on a sheet representing its layout. The accumulated data would then be reviewed by the manager. Clearly, this is a labor-intensive way of gathering such data. It is also potentially annoying to customers if the employees tracking them are not very discrete. Traffic flow also is assessed utilizing electronic or mechanical methods, such as by the use of turn-styles, electronic beams and other known techniques. However, these “people counters” are of limited value since they generally only ascertain the number of people passing a specific location without regard to the identities or demographics of those people. Employees, and sometimes non-humans, such as pets, passing by such counters also are included in the data. Sales or purchasing data (e.g., via frequent shopper type cards) also may be utilized to estimate traffic flow.
In addition to in-store traffic flow studies and measures, owners, managers, distributors, etc., of commercial establishments are further interested in tracking retail customers' other patterns of movements, such as customers' travel and store visiting patterns prior to entry and after exiting such commercial establishments. This information enables them to assess, among other things, which stores represent their greatest competition. Current studies employ surveys and shopping diaries that require customers to manually record their travel patterns, identifying the stores they visited and for how long. Similar to the above-mentioned manually performed in-store traffic flow studies, surveys and shopping diaries are also labor-intensive and are generally not popular. Many consumers find it difficult to complete surveys and shopping diaries, often making omissions or over-statements when recording their activity and travel patterns.
It is desired, therefore, to provide less expensive, less labor-intensive and less potentially annoying ways to gather in-store and out-of-store traffic flow data. In addition, owners and managers of commercial establishments as well as manufacturers and distributors would like to obtain reports from which they can evaluate the effectiveness of their advertising expenditures, based not only on such traffic flow data but also on media exposure data and the like. Such reports would in turn enable retailers to better understand the shopping characteristics and habits of customers in ways previously not possible.